Functional emulation of biological synapses using electronic devices is regarded as the first step toward neuromorphic engineering and artificial neural networks (ANNs). Electrolyte-gated transistors (EGTs) are mixed ionic–electronic conductivity devices capable of efficient gate-channel capacitance coupling, biocompatibility, and flexible architectures. Electrolyte gating offers significant advantages for the realization of neuromorphic devices/architectures, including ultralow-voltage operation and the ability to form parallel-interconnected networks with minimal hardwired connectivity. In this review, the most recent developments in EGT-based electronics are introduced with their synaptic behaviors and detailed mechanisms, including short-/long-term plasticity, global regulation phenomena, lateral coupling between device terminals, and spatiotemporal correlated functions. Analog memory phenomena allow for the implementation of perceptron-based ANNs. Due to their mixed-conductivity phenomena, neuromorphic circuits based on EGTs allow for facile interfacing with biological environments. We also discuss the future challenges in implementing low power, high speed, and reliable neuromorphic computing for large-scale ANNs with these neuromorphic devices. The advancement of neuromorphic devices that rely on EGTs highlights the importance of this field for neuromorphic computing and for novel healthcare technologies in the form of adaptable or trainable biointerfacing.

Sensing and recognizing invisible ultraviolet (UV) light is vital for exploiting advanced artificial visual perception system. However, due to the uncertainty of the natural environment, the UV signal is very hard to be detected and perceived. Here, inspired by the tetrachromatic visual system, we report a controllable UV-ultrasensitive neuromorphic vision sensor (NeuVS) that uses organic phototransistors (OPTs) as the working unit to integrate sensing, memory and processing functions. Benefiting from asymmetric molecular structure and unique UV absorption of the active layer, the as fabricated UV-ultrasensitive NeuVS can detect 370 nm UV-light with the illumination intensity as low as 31 nW cm⁻², exhibiting one of the best optical figures of merit in UV-sensitive neuromorphic vision sensors. Furthermore, the NeuVS array exbibits good image sensing and memorization capability due to its ultrasensitive optical detection and large density of charge trapping states. In addition, the wavelength-selective response and multi-level optical memory properties are utilized to construct an artificial neural network for extract and identify the invisible UV information. The NeuVS array can perform static and dynamic image recognition from the original color image by filtering red, green and blue noise, and significantly improve the recognition accuracy from 46 to 90%.